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Record W1999071553 · doi:10.1177/1474515111429656

Caring for the radial artery post-angiogram: a pilot study on a comparison of three methods of compression

2012· article· en· W1999071553 on OpenAlexaff
Jennifer C Fech, Robert C. Welsh, Kathleen Hegadoren, Colleen M. Norris

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineRadial arteryHemostasisFemoral arteryLimitingCompression (physics)RadiologyArterySurgeryVascular closure device

Abstract

fetched live from OpenAlex

BACKGROUND: A coronary angiogram, a diagnostic tool to visualize the coronary anatomy, has traditionally been accessed through the femoral artery. However, in the last 20 years, the radial artery has gained more popularity among physicians and patients, offering an alternative to the femoral approach. Various methods of applying compression to the radial puncture site have been used, but no research has been done to demonstrate the most effective way of achieving hemostasis while limiting complications and ensuring the efficient use of nursing and medical resources. OBJECTIVE: The purpose of this pilot study was to compare two devices and three methods for achieving hemostasis after a transradial angiogram while assessing vascular complications and time endpoints. DESIGN AND METHODS: A mechanical device (Terumo™ wristband) and a hydrophilic wound dressing (Clo-Sur P.A.D.) were used. The Terumo band was studied twice, using the current method and a fast-release method. RESULTS: Taking into account the small sample size of this pilot study (N = 25 per group), statistically significant differences (p ≤ 0.005) are seen in time to discharge in the fast-release Terumo (134.0 minutes) and Clo-Sur P.A.D. groups (113.7 minutes), as compared with the control Terumo group (178.2 minutes), without increasing vascular complications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.118
GPT teacher head0.381
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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